Factors Associated With Length of Stay for Hip and Knee Arthroplasty: A 20-Year Single-Province Population-Based Analysis of Longitudinal Temporal Trends
Bibliographic record
Abstract
Background With notable benefits in reducing length of stay (LOS), this study aimed to quantify the temporal trend and the factors contributing to increased LOS for primary and revision total hip (THA and rTHA) and knee (TKA and rTKA) arthroplasty. The study was carried out for a large population-based cohort over a 20-year period. Methods This was a retrospective population-based study assessing the LOS for all primary and revision THA and TKA procedures between 2003 and 2022. The primary outcome of interest was LOS. Univariate and multivariate analyses were performed to identify associated variables. Results For the entire dataset, there were 16,677 primary THAs, 13,018 primary TKAs, 3276 (aseptic) rTHAs, 1445 (aseptic) rTKAs, 820 (septic) rTHAs, and 667 (septic) rTKAs. The median LOS over the 20-year period between 2003 and 2022 demonstrated a steady and continuous decline from a median of 5 days (interquartile range 3-7) in 2003 to 1 day (interquartile range 1-2) in 2022. On multivariate analysis, there were a number of factors associated with increasing LOS: year of procedure ( P < .0001), procedure type ( P < .0001), age ( P < .0001), and American Society of Anesthesiologists class ( P < .0001). On multivariate analysis, body mass index was not associated with increased LOS ( P = .5631). Conclusions There was a downward trend in LOS for all types of primary and revision THA and TKA. The factors contributing most to a reduction in LOS include the year the procedure was performed, primary THA procedures, aseptic (vs periprosthetic joint infection) revision procedures, younger age, and lower American Society of Anesthesiologists classes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".